<<<<<<< HEAD <<<<<<< HEAD ======= >>>>>>> d22c9825d0bd505fe432c87683427243af425b83 A study of early predictors (Comorbidities and Etiology) of patient’s outcome after cirrhosis hospitalization ======= A study of early predictors (Comorbidities and Etiology) of patient’s outcome after cirrhosis hospitalization A study of early predictors (Comorbidities and Etiology) of patient’s outcome after cirrhosis hospitalization >>>>>>> 8b6565eda2a8fcf430700d631dc4f593685717a1

A study of early predictors (Comorbidities and Etiology) of patient’s outcome after cirrhosis hospitalization

Group 19

Eva Frossard, Pauline Charpentier, Noy Tabul, Fabian Ziegler

Introduction

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Objective: Understand patterns and correlations between early predictors Comorbidities and Etiologies, their numerical estimate CPS and Charlson index and cirrhosis’ patients outcome  

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Objective: Understand patterns and correlations between early predictors Comorbidities and Etiologies, their numerical estimate CPS and Charlson index and cirrhosis’ patients outcome  

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Objective: Understand patterns and correlations between early predictors Comorbidities and Etiologies, their numerical estimate CPS and Charlson index and cirrhosis’ patients outcome  

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Cirrhosis = condition in which the liver is scarred and permanently damaged.

Relevancy of the study: Cirrhosis is a leading cause of mortality in the world (11th), and finding accurate descriptive index is a useful tool to investigate the possible outcome of patients.

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Materials - Dataset

Data set used: Early predictors of outcomes of hospitalization for cirrhosis and assessment of the impact of race and ethnicity at safety-net hospitals

  • 733 patients
  • From 4 safety-net hospitals in the US
  • Male dominated study (67.31%)
  • Predominant age group [60; 70]
  • Main liver disease diagnosis
    • Ascites (31%)
    • Hepatic encephalopathy (21.2%)
    • UGIB (19.9%)
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Methods

Cleaning:

  • Extracting, renaming, reordering columns
  • Dropping non-available values them

Tidying:

  • Pivot_wider on Comorbidities
  • Pivot_wider on Etiologies

Augmenting:

  • Created categories for Mortality: no death, death in hospital, after 30 days, after 90 days
  • Creating age bins instead decade
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Overview of comorbidities and etiologies

  • Etiology is the study of the factors that come together leading to a disease.

  • Comorbidity is an additional disease that can interact and coexist simultaneously with cirrhosis.

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Results: Different outcomes tendency depending on the liver disease

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  • Bar plot which shows liver disease distribution, filled by mortality status

  • Demonstrates the impact of varying mortality rates base on liver diseases

  • Reveals differences in mortality timing between “Other fluid overload” and “SNB”

Death ratios of every Etiology/Liver disease combination

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  • Tile chart reveals ratios for liver diseases and associate etiologies

  • High mortality for specific combinations like Hepatic encephalopathy/Auto-immune hepatitis.

  • Lower death ratios (<25%) for example for Hepatitis B or Non-alcoholic fatty liver disease.

  • Overrepresentation of certain comorbidities

Model 1 - Logistic regression

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  • Overview: Logistic regression model

  • Variables: CPS & Mortality

  • Results: For every increased CPS unit, the likelihood for mortality increase by 50 %

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    • Overview: Logistic regression model

    • Variables: CPS & Mortality

    • Results: For every increased CPS unit, the likelihood for mortality increase by 50 %

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Model 2 - Linear regression

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  • Overview: Linear regression model

  • Variables: CPS & Etiologies

  • Results: Statistically significant result for nafld associated with low CPS

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  • Overview: Linear regression model

  • Variables: CPS & Etiologies

  • Results: Statistically significant result for nafld associated with low CPS

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Conclusion

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  • The type of the liver disease notably impacts the mortality

  • The difference in timing hint a varying disease severity

  • Lethal combinations of liver diseases and their associated etiologies can be identified

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  • For every CPS unit increase there is a 50 % higher likelihood for mortality

  • According to the linear regression nafld is associated with low CPS

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